Browsing by Author "Kaski, Kimmo K."

Now showing 1 - 10 of 10

Detailed-level modelling of influence spreading on complex networks
(2024-12) Kuikka, Vesa; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.
Dynamics of hierarchical weighted networks of van der Pol oscillators
(2020-12-01) Monsivais-Velazquez, Daniel; Bhattacharya, Kunal; Barrio, Rafael A.; Maini, Philip K.; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We investigate the dynamics of regular fractal-like networks of hierarchically coupled van der Pol oscillators. The hierarchy is imposed in terms of the coupling strengths or link weights. We study the low frequency modes, as well as frequency and phase synchronization, in the network by a process of repeated coarse-graining of oscillator units. At any given stage of this process, we sum over the signals from the oscillator units of a clique to obtain a new oscillating unit. The frequencies and the phases for the coarse-grained oscillators are found to progressively synchronize with the number of coarse-graining steps. Furthermore, the characteristic frequency is found to decrease and finally stabilize to a value that can be tuned via the parameters of the system. We compare our numerical results with those of an approximate analytic solution and find good qualitative agreement. Our study on this idealized model shows how oscillations with a precise frequency can be obtained in systems with heterogeneous couplings. It also demonstrates the effect of imposing a hierarchy in terms of link weights instead of one that is solely topological, where the connectivity between oscillators would be the determining factor, as is usually the case.
Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
(2022-08) Kuikka, Vesa; Aalto, Henrik; Ijäs, Matias; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Modelling interactions on complex networks needs efficient algorithms for describing processes on a detailed level in the network structure. This kind of modelling enables more realistic applications of spreading processes, network metrics, and analyses of communities. However, different real-world processes may impose requirements for implementations and their efficiency. We discuss different transmission and spreading processes and their interrelations. Two pseudo-algorithms are presented, one for the complex contagion spreading mechanism using non-self-avoiding paths in the modelling, and one for simple contagion processes using self-avoiding paths in the modelling. The first algorithm is an efficient implementation that can be used for describing social interaction in a social network structure. The second algorithm is a less efficient implementation for describing specific forms of information transmission and epidemic spreading.
Influence spreading model in analysing ego-centric social networks
(2022-02-15) Kuikka, Vesa; Monsivais, Daniel; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In an earlier study one of us had developed a model of influence spreading for analysing human behaviour and interaction with others in a social network. Here we apply this model and corresponding influence centrality measures to real data of mobile phone call detail records. From this we get structures of human ego-centric networks and use a simple model, based on the number of phone calls, to describe the strengths of social relationships. To analyse 48,000 egos in their ego-centric networks we define normalised out-centrality and in-centrality influence measures, by dividing with out-degree and in-degree, respectively. With these and the betweenness centrality measures, we analyse the influence spreading in the ego-centric networks under different scenarios of link strengths between individuals reflecting the network structure being either interaction or connectivity oriented. The model reveals characteristics of social behaviour that are not obvious from the data analysis of raw empirical data or from the results of standard centrality measures. A transition is discovered in behaviour from young to older age groups for both genders and in both normalised out-centrality and in-centrality as well as betweenness centrality results.
A model for social spreading of Covid-19: Cases of Mexico, Finland and Iceland
(2021-11-15) Barrio, Rafael A.; Kaski, Kimmo K.; Haraldsson, Guđmundur G.; Aspelund, Thor; Govezensky, Tzipe
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.
A modelling study to explore the effects of regional socio-economics on the spreading of epidemics
(2024-12) Snellman, Jan E.; Barrio, Rafael A.; Kaski, Kimmo K.; Korpi–Lagg, Maarit J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Epidemics, apart from affecting the health of populations, can have large impacts on their social and economic behavior and subsequently feed back to and influence the spreading of the disease. This calls for systematic investigation which factors affect significantly and either beneficially or adversely the disease spreading and regional socio-economics. Based on our recently developed hybrid agent-based socio-economy and epidemic spreading model we perform extensive exploration of its six-dimensional parameter space of the socio-economic part of the model, namely, the attitudes towards the spread of the pandemic, health and the economic situation for both, the population and government agents who impose regulations. We search for significant patterns from the resulting simulated data using basic classification tools, such as self-organizing maps and principal component analysis, and we monitor different quantities of the model output, such as infection rates, the propagation speed of the epidemic, economic activity, government regulations, and the compliance of population on government restrictions. Out of these, the ones describing the epidemic spreading were resulting in the most distinctive clustering of the data, and they were selected as the basis of the remaining analysis. We relate the found clusters to three distinct types of disease spreading: wave-like, chaotic, and transitional spreading patterns. The most important value parameter contributing to phase changes and the speed of the epidemic was found to be the compliance of the population agents towards the government regulations. We conclude that in compliant populations, the infection rates are significantly lower and the infection spreading is slower, while the population agents’ health and economical attitudes show a weaker effect.
Modelling the interplay between epidemics and regional socio-economics
(2022-10-15) Snellman, Jan E.; Barrio, Rafael A.; Kaski, Kimmo K.; Käpylä, Maarit J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In this study we present a dynamical agent-based model to investigate the interplay between the socio-economy of and SEIRS-type epidemic spreading over a geographical area, divided to smaller area districts and further to smallest area cells. The model treats the populations of cells and authorities of districts as agents, such that the former can reduce their economic activity and the latter can recommend economic activity reduction both with the overall goal to slow down the epidemic spreading. The agents make decisions with the aim of attaining as high socio-economic standings as possible relative to other agents of the same type by evaluating their standings based on the local and regional infection rates, compliance to the authorities’ regulations, regional drops in economic activity, and efforts to mitigate the spread of epidemic. We find that the willingness of population to comply with authorities’ recommendations has the most drastic effect on the epidemic spreading: periodic waves spread almost unimpeded in non-compliant populations, while in compliant ones the spread is minimal with chaotic spreading pattern and significantly lower infection rates. Health and economic concerns of agents turn out to have lesser roles, the former increasing their efforts and the latter decreasing them.
Social structure formation in a network of agents playing a hybrid of ultimatum and dictator games
(2021) Snellman, Jan E.; Barrio, Rafael A.; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Here we present an agent-based model where agents interact with other agents by playing a hybrid of dictator and ultimatum games in a co-evolving social network. The basic assumption about the behaviour of the agents in both games is that they try to attain superior socioeconomic positions relative to other agents. As the model parameters we have chosen the relative proportions of the dictator and ultimatum game strategies being played between a pair of agents in a single social transaction and a parameter depicting the living costs of the agents. The motivation of the study is to examine how different types of social interactions affect the formation of social structures and networks, when the agents have a tendency to maximise their socioeconomic standing. We find that such social networks of agents invariably undergo a community formation process from simple chain-like structure to more complex networks as the living cost parameter is increased. The point where this occurs, depends also on the relative proportion of the dictator and ultimatum games being played. We find that it is harder for complex social structures to form when the dictator game strategy in social transactions of agents becomes more dominant over that of the ultimatum game.
Socio-economic pandemic modelling : case of Spain
(2024-01) Snellman, Jan E.; Barreiro, Nadia L.; Barrio, Rafael A.; Ventura, Cecilia I.; Govezensky, Tzipe; Kaski, Kimmo K.; Korpi-Lagg, Maarit J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.
Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification
(2022) Jaskari, Joel; Sahlsten, Jaakko; Damoulas, Theodoros; Knoblauch, Jeremias; Sarkka, Simo; Karkkainen, Leo; Hietala, Kustaa; Kaski, Kimmo K.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian neural networks (BNNs) have been proposed for this task, but previous studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results for 9 BNNs by systematically investigating a clinical dataset and 5-class classification scheme, together with benchmark datasets and binary classification scheme. Moreover, we derive a connection between entropy-based uncertainty measure and classifier risk, from which we develop a novel uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure improves performance on the clinical dataset for the binary classification scheme, but not to such an extent as on the benchmark datasets. It improves performance in the clinical 5-class classification scheme for the benchmark datasets, but not for the clinical dataset. Our novel uncertainty measure generalizes to the clinical dataset and to one benchmark dataset. Our findings suggest that BNNs can be utilized for uncertainty estimation in classifying diabetic retinopathy on clinical data, though proper uncertainty measures are needed to optimize the desired performance measure. In addition, methods developed for benchmark datasets might not generalize to clinical datasets.